FastUMI: A Scalable and Hardware-Independent Universal Manipulation Interface with Dataset

Abstract

Real-world manipulation datasets for robotic arms remain scarce due to the high costs, rigid hardware dependencies, and complex setup procedures associated with existing data collection methods. We introduce, a redesigned Universal Manipulation Interface (UMI) that addresses these challenges, enabling low-cost, scalable, and rapid deployment across heterogeneous platforms. FastUMI achieves this through: (i) hardware decoupling via extensive mechanical reengineering, which removes dependence on specialized robotic components while preserving a consistent visual perspective; (ii) replacement of complex visual–inertial odometry with a commercial off-the-shelf tracker, simplifying the software stack without compromising pose estimation accuracy; and (iii) the provision of an integrated ecosystem that streamlines data acquisition, automates quality control, and ensures compatibility with both standard and enhanced imitation-learning pipelines. To facilitate further research, we release an open-access dataset comprising over 15,000 real-world demonstrations spanning 24 tasks constituting one of the most extensive UMI-like resources to date. Empirical evaluations show that FastUMI supports rapid deployment, reduces operational overhead, and delivers robust performance across diverse manipulation scenarios, advancing scalable data-driven robotic learning.